Contextual Constraint Modification for Multi-Master Data Sharing
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Solution Overview
Problem
Distributed database systems with multi-master replication face challenges such as increased latency due to conflict resolution and access control issues, particularly in heterogeneous databases, which complicate data sharing and retrieval processes.
Innovation Solution
A computer-implemented method for modifying data sharing parameters and merging data models across heterogeneous databases using constraint modification logic, shareable model generation, and data model merging logic to reduce latency and improve data sharing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-master replication is implemented in distributed database systems, then data sharing capability is improved, but latency increases due to conflict resolution and access control issues
Solution Approach 1:
The system dynamically adjusts data sharing constraints based on context. Constraint modification logic evaluates contextual information about data requests and modifies sharing parameters in real-time, allowing the system to be more adaptable without permanently increasing latency. The dynamic nature enables selective relaxation of constraints when safe to do so.
Solution Approach 2:
The patent changes the parameters of data sharing by introducing contextual modification. Instead of fixed access control parameters, the system modifies sharing constraints based on contextual factors such as data type, user permissions, and system state. This parameter change approach resolves the contradiction by making parameters flexible rather than static.
2Adaptability or versatility
If heterogeneous databases are used in the distributed system, then data model diversity is improved, but conflict resolution complexity increases
Solution Approach 1:
The constraint modification logic acts as an intermediary between heterogeneous databases. It translates and reconciles different data models by applying contextual rules, thereby managing conflict resolution complexity centrally rather than requiring complex point-to-point resolution between each database pair. This intermediary approach maintains data model diversity while controlling overall system complexity.
Solution Approach 2:
The system segments conflict resolution into manageable contexts. Rather than resolving all conflicts globally with a single complex mechanism, the patent divides conflict resolution into context-specific segments handled by constraint modification logic. Each context can have tailored resolution rules, reducing overall complexity while supporting diverse data models.
3Reliability
If data sharing constraints are enforced across all databases, then data integrity is improved, but data sharing efficiency decreases
Solution Approach 1:
The system applies partial action by selectively enforcing constraints rather than universally applying them. Constraint modification logic evaluates each data sharing request and applies constraints only when necessary for integrity, allowing efficient sharing when constraints can be safely relaxed. This partial enforcement maintains integrity while improving efficiency.
Solution Approach 2:
Data sharing constraints transition from static to dynamic. The system continuously evaluates contextual information and adjusts constraint enforcement accordingly. When context indicates safety, constraints are relaxed to improve efficiency; when context indicates risk, constraints are enforced to maintain integrity. This dynamic adjustment resolves the efficiency-integrity trade-off.
Data Source
AI summary
A method of contextual modification of data sharing constraints is disclosed. The method comprises receiving a data sharing request to share a first data model with a database associated with a second data model; generating a shareable version of the first data model in response to the data sharing request; determining a parameter value used to perform a data model merging operation to merge the shareable version of the first data model with the second data model, the parameter value indicating whether to execute or skip a particular process during the data model merging operation; determining context data for the data model merging operation based on the generating; modifying the parameter value based on the context data; performing the data model merging operation using the modified parameter value.